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CN113887984A - Early warning reminding method and device based on enterprise credit investigation blacklist and electronic equipment - Google Patents

Early warning reminding method and device based on enterprise credit investigation blacklist and electronic equipment Download PDF

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CN113887984A
CN113887984A CN202111199241.XA CN202111199241A CN113887984A CN 113887984 A CN113887984 A CN 113887984A CN 202111199241 A CN202111199241 A CN 202111199241A CN 113887984 A CN113887984 A CN 113887984A
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enterprise
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credit
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王春雷
崔华志
王�琦
邹政权
张海波
方璐
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Heilongjiang Paradigm Intelligent Technology Co ltd
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Heilongjiang Paradigm Intelligent Technology Co ltd
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Abstract

The embodiment of the invention discloses an early warning reminding method based on an enterprise credit investigation blacklist, which comprises the following steps: extracting enterprise credit data from a plurality of related departments, and capturing enterprise public opinion data through a network; cleaning and carrying out format classification processing on the data to obtain data to be processed; and inputting the data to be processed into the trained early warning analysis model for early warning analysis, obtaining an analysis result, and performing early warning prompt. By implementing the embodiment of the invention, the enterprise credit data is extracted from a plurality of related departments, the enterprise public opinion data is captured through the network, and is cleaned and subjected to the formal classification treatment, so that the comprehensiveness, integrity and authenticity of the credit data acquisition can be ensured, the extracted data has higher reference value, and the problem that the information collection and aggregation of the enterprise losing the trust in the society in the prior art are not comprehensive and untimely is solved; meanwhile, early warning analysis and early warning prompt are carried out through the trained early warning analysis model, and timely early warning prompt can be carried out on enterprises with the risk of losing confidence.

Description

Early warning reminding method and device based on enterprise credit investigation blacklist and electronic equipment
Technical Field
The invention relates to the technical field of computer software, in particular to an early warning reminding method and device based on an enterprise credit investigation blacklist and electronic equipment.
Background
Most of the existing blacklisting mechanisms are concentrated between related departments of the country and banks. For other related agencies and industries, there is temporarily no robust blacklist acquisition mechanism. In the existing blacklist acquisition mechanism, the acquisition of enterprise information is not comprehensive enough, and most of the result data obtained after various kinds of information loss or overdue do not provide timely risk early warning for enterprises.
Disclosure of Invention
Aiming at the technical defects in the prior art, the embodiment of the invention aims to provide an early warning reminding method and device based on an enterprise credit investigation blacklist, electronic equipment and a storage medium.
In order to achieve the above object, in a first aspect, an embodiment of the present invention provides an early warning and reminding method based on an enterprise credit investigation blacklist, including:
extracting enterprise credit data from a plurality of related departments, and capturing enterprise public opinion data through a network;
cleaning and carrying out format classification processing on the enterprise credit data and the enterprise public opinion data to obtain data to be processed;
and inputting the data to be processed into a trained early warning analysis model for early warning analysis to obtain an analysis result, and performing early warning prompt according to the analysis result.
As a specific embodiment of the present application, the extracting of the enterprise credit data from the plurality of department of correlation doors is specifically:
and extracting the enterprise credit data from the credit granting platform, the industry and commerce department, the law or the tax department.
As a specific embodiment of the present application, the data to be processed includes enterprise business data, enterprise legal data, credit loss data, financial data, tax data and enterprise high-management personal information; the early warning reminding method comprises the following steps:
inputting the enterprise operation data, the enterprise legal data and the information loss data into the early warning analysis model, carrying out comprehensive evaluation on the enterprise, judging whether the enterprise has fraud risk, and if the enterprise has fraud risk and has higher risk level, listing the enterprise into a blacklist and carrying out risk prompt;
inputting the high-management personal information data into the early warning analysis model, judging whether the high-management person has legal risk or not, and if the legal risk exists and the legal risk affects enterprises, carrying out risk prompt;
and inputting the financial data and the tax data into the early warning analysis model, judging whether the enterprise has financial risks and/or tax risks, and if the financial risks and the tax risks exist and the risk value is higher than a preset threshold value, carrying out risk prompt.
As a preferred embodiment of the present application, the method further includes training an early warning analysis model, specifically:
constructing an early warning analysis model based on an artificial intelligence technology;
obtaining sample data from related government departments, banking institutions and credit granting platforms, wherein the sample data comprises enterprise credit data and industry data;
and training the early warning analysis model by adopting the sample data.
In a second aspect, an embodiment of the present invention discloses an early warning and reminding device based on an enterprise credit investigation blacklist, including:
the data acquisition unit is used for extracting enterprise credit data from a plurality of related departments and capturing enterprise public opinion data through a network;
the processing unit is used for cleaning and carrying out format classification processing on the enterprise credit data and the enterprise public opinion data to obtain data to be processed;
and the early warning analysis unit is used for inputting the data to be processed into a trained early warning analysis model for early warning analysis to obtain an analysis result and carrying out early warning prompt according to the analysis result.
As a specific embodiment of the present application, the data to be processed includes enterprise business data, enterprise legal data, credit loss data, financial data, tax data and enterprise high-management personal information; the early warning analysis unit is specifically configured to:
inputting the enterprise operation data, the enterprise legal data and the information loss data into the early warning analysis model, carrying out comprehensive evaluation on the enterprise, judging whether the enterprise has fraud risk, and if the enterprise has fraud risk and has higher risk level, listing the enterprise into a blacklist and carrying out risk prompt;
inputting the high-management personal information data into the early warning analysis model, judging whether the high-management person has legal risk or not, and if the legal risk exists and the legal risk affects enterprises, carrying out risk prompt;
and inputting the financial data and the tax data into the early warning analysis model, judging whether the enterprise has financial risks and/or tax risks, and if the financial risks and the tax risks exist and the risk value is higher than a preset threshold value, carrying out risk prompt.
As a preferred embodiment of the present application, the early warning analysis system further includes a training unit, configured to train an early warning analysis model, specifically:
constructing an early warning analysis model based on an artificial intelligence technology;
obtaining sample data from related government departments, banking institutions and credit granting platforms, wherein the sample data comprises enterprise credit data and industry data;
and training the early warning analysis model by adopting the sample data.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other through a bus, and the memory is used to store a computer program, and the computer program includes program instructions. Wherein the processor is configured to invoke the program instructions to perform the method of the first aspect.
In a fourth aspect, embodiments of the present invention provide a computer-readable storage medium having stored thereon a computer program/instructions. Which when executed by a processor performs the steps of the method as described in the first aspect above.
By implementing the embodiment of the invention, the enterprise credit data is extracted from a plurality of related departments, the enterprise public opinion data is captured through the network, and is cleaned and subjected to the formal classification treatment, so that the comprehensiveness, integrity and authenticity of the credit data acquisition can be ensured, the extracted data has higher reference value, and the problem that the information collection and aggregation of the enterprise losing the trust in the society in the prior art are not comprehensive and untimely is solved; meanwhile, early warning analysis and early warning prompt are carried out through the trained early warning analysis model, and timely early warning prompt can be carried out on enterprises with the risk of losing confidence.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below.
Fig. 1 is a flowchart of an early warning and reminding method based on an enterprise credit investigation blacklist according to an embodiment of the present invention;
fig. 2 is a structural diagram of an early warning device based on an enterprise credit investigation blacklist according to an embodiment of the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an early warning reminding method based on an enterprise credit blacklist according to an embodiment of the present invention includes:
and S101, extracting enterprise credit data from the multiple department of correlation doors.
And S102, capturing enterprise public opinion data through a network.
The enterprise public opinion data refers to public opinion information and public discussion, report and reflection of any topic related to an enterprise.
And S103, cleaning and carrying out format classification processing on the enterprise credit data and the enterprise public opinion data to obtain data to be processed.
Specifically, enterprise credit data is extracted from credit granting platforms, industry and commerce, law, finance or tax departments, and the like. In the acquisition process, the data are subjected to cross validation, cleaning, formatting classification processing and the like, so that the authenticity and the comprehensiveness of the data are ensured.
And S104, training an early warning analysis model.
Specifically, an artificial intelligence technology is used for constructing an early warning analysis model, sample data (including enterprise credit data and industry data) is obtained from relevant government departments, bank organizations and credit granting platforms, and the early warning analysis model is trained by the sample data, so that the early warning analysis model is closer to the industry requirements.
And S105, inputting the data to be processed into the trained early warning analysis model for early warning analysis to obtain an analysis result, and performing early warning prompt according to the analysis result.
The data to be analyzed comprises but is not limited to enterprise operation data, enterprise legal data, credit loss data, financial data, tax data, enterprise high-management personal information and the like; step S105 specifically includes:
inputting the enterprise operation data, the enterprise legal data and the information loss data into the early warning analysis model, carrying out comprehensive evaluation on the enterprise, judging whether the enterprise has fraud risk, and if the enterprise has fraud risk and has higher risk level, listing the enterprise into a blacklist and carrying out risk prompt;
inputting the high-management personal information data into the early warning analysis model, judging whether the high-management person has legal risk or not, and if the legal risk exists and the legal risk affects enterprises, carrying out risk prompt;
and inputting the financial data and the tax data into the early warning analysis model, judging whether the enterprise has financial risks and/or tax risks, and if the financial risks and the tax risks exist and the risk value is higher than a preset threshold value, carrying out risk prompt.
When the financial risk and/or the tax risk are analyzed, the adopted method mainly comprises the following steps:
(1) report analysis method
The cash flow table shows that the enterprise ' cash inflow of operation activities > outflow and ' cash inflow of investment activities ' outflow show that the enterprise operation activities and financing activities can both generate cash net inflow and the financial condition is stable. In the rapid development period, the investment of enterprises in medium and long periods is expanded, and the condition that the cash outflow amount is larger than the cash inflow amount in the investment activity in a short period is more normal.
(2) Index analysis method
The index analysis method is a technical method for calculating, comparing and analyzing the related indexes of the enterprise financial risk according to the data provided by the enterprise financial accounting, statistical accounting, business accounting data and other aspects, and searching, identifying and discovering the risk from the analysis result. The method may include the following two aspects:
A. asset management analysis: by calculating the turnover rates of the accounts receivable, the inventory, the flowing assets and the non-flowing assets and the total assets of the enterprise, if the turnover rates are all reduced, the asset management capability of the enterprise is reduced.
B. And (3) carrying out profit capacity analysis: through the calculation of sales profit rate, asset profit rate and equity profit margin rate, the profit capacity is obviously reduced, but in combination with the analysis of the industry situation, the reduction situation can be obtained normally due to the factors of the previous expansion, the supply and demand of the whole industry and the like.
It should be noted that the determining the possible financial risk mainly includes:
(1) risk of investment
The possibility that the enterprise cannot recover the investment after the investment due to the economic loss is generated, and the expected income cannot be realized. Investment activities are important links of financial management activities, and whether investment decisions are correct or not relates to life and death of enterprises.
(2) Unreasonable capital structure
The capital structure is unreasonable, the proportion of the long-term liability to the total liability is severely imbalanced with the proportion of the short-term liability, and a considerable portion of the non-mobile assets are financed and purchased by mobile liabilities. The possibility of unreliabilities is easily caused by excessive mobile liabilities, so that the financial crisis is caused. From a financial management perspective, normal capital use would be to purchase mobile assets with mobile liabilities, and in particular fixed assets with own funds or long term liabilities. But due to unreasonable capital structure and the accelerated expansion of enterprises, a large number of fixed assets are purchased, and the situation of purchasing non-mobile assets by mobile liabilities occurs, so that financial risks are caused and increased.
(3) Capital recovery
The receivables management is an important content of the financial risk management, the sales rate is reduced, the stock rate is increased, the stock occupies excessive mobile funds, the fund backflow barrier is caused, the cash flow of the whole enterprise is influenced, and the financial risk is brought to the enterprise.
As can be seen from the above description, the early warning reminding method based on the enterprise credit investigation blacklist provided by the embodiment of the invention extracts the enterprise credit data from a plurality of related departments, captures the enterprise public opinion data through the network, and performs cleaning and stylized classification processing on the enterprise public opinion data, so that the comprehensiveness, integrity and authenticity of the credit data acquisition can be ensured, the extracted data has higher reference value, and the problem that the information collection and aggregation of the enterprise losing credit in the society is not comprehensive and not timely in the prior art is solved; meanwhile, early warning analysis and early warning prompt are carried out through the trained early warning analysis model, and timely early warning prompt can be carried out on enterprises with the risk of losing confidence.
Based on the same inventive concept, the embodiment of the invention provides an early warning reminding device based on an enterprise credit investigation blacklist. As shown in fig. 2, the apparatus includes:
the data acquisition unit 10 is used for extracting enterprise credit data from a plurality of related departments and capturing enterprise public opinion data through a network;
the processing unit 11 is used for cleaning and carrying out format classification processing on the enterprise credit data and the enterprise public opinion data to obtain data to be processed;
the training unit 12 is used for training an enterprise credit line analysis model;
and the early warning analysis unit 13 is used for inputting the data to be processed into a trained early warning analysis model for early warning analysis to obtain an analysis result, and performing early warning prompt according to the analysis result.
Wherein, the training unit 12 is specifically configured to:
constructing an early warning analysis model based on an artificial intelligence technology;
obtaining sample data from related government departments, banking institutions and credit granting platforms, wherein the sample data comprises enterprise credit data and industry data;
and training the early warning analysis model by adopting the sample data.
The data to be processed comprises enterprise operation data, enterprise legal data, credit loss data, financial data, tax data and enterprise high-management personal information;
the early warning analysis unit 13 is specifically configured to:
inputting the enterprise operation data, the enterprise legal data and the information loss data into the early warning analysis model, carrying out comprehensive evaluation on the enterprise, judging whether the enterprise has fraud risk, and if the enterprise has fraud risk and has higher risk level, listing the enterprise into a blacklist and carrying out risk prompt;
inputting the high-management personal information data into the early warning analysis model, judging whether the high-management person has legal risk or not, and if the legal risk exists and the legal risk affects enterprises, carrying out risk prompt;
and inputting the financial data and the tax data into the early warning analysis model, judging whether the enterprise has financial risks and/or tax risks, and if the financial risks and the tax risks exist and the risk value is higher than a preset threshold value, carrying out risk prompt.
Optionally, the embodiment of the invention further provides an electronic device. As shown in fig. 3, the data processing apparatus may include: one or more processors 101, one or more input devices 102, one or more output devices 103, and memory 104, the processors 101, input devices 102, output devices 103, and memory 104 being interconnected via a bus 105. The memory 104 is used for storing a computer program comprising program instructions, and the processor 101 is configured to call the program instructions to execute the method of the above-mentioned embodiment part of the enterprise credit blacklist-based warning alert method.
It should be understood that, in the embodiment of the present invention, the Processor 101 may be a Central Processing Unit (CPU), and the Processor may also be other general processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The input device 102 may include a keyboard or the like, and the output device 103 may include a display (LCD or the like), a speaker, or the like.
The memory 104 may include read-only memory and random access memory, and provides instructions and data to the processor 101. A portion of the memory 104 may also include non-volatile random access memory. For example, the memory 104 may also store device type information.
In a specific implementation, the processor 101, the input device 102, and the output device 103 described in this embodiment of the present invention may execute the implementation manner described in the embodiment of the early warning reminding method based on the enterprise credit investigation blacklist provided in this embodiment of the present invention, which is not described herein again.
It should be noted that, for a more specific workflow of the early warning and reminding device and the electronic device based on the enterprise credit blacklist, please refer to the foregoing embodiment of the method, which is not described herein again.
As can be seen from the above description, the early warning reminding device and the electronic device based on the enterprise credit investigation blacklist provided by the embodiment of the invention extract the enterprise credit data from a plurality of related departments, capture the enterprise public opinion data through the network, and perform cleaning and stylized classification processing on the enterprise public opinion data, so that the comprehensiveness, integrity and authenticity of the credit data acquisition can be ensured, the extracted data has higher reference value, and the problem that the information collection and aggregation of the enterprise losing credit in the society in the prior art are not comprehensive and timely is solved; meanwhile, early warning analysis and early warning prompt are carried out through the trained early warning analysis model, and timely early warning prompt can be carried out on enterprises with the risk of losing confidence.
Further, an embodiment of the present invention also provides a readable storage medium, on which a computer program/instruction is stored, which when executed by a processor implements: the method of the method embodiment section above.
Further, embodiments of the present invention provide a computer program product having a computer program/instructions stored thereon. The computer program/instructions when executed by the processor implement: the method of the method embodiment section above.
The computer program product is to be understood as a software product, the solution of which is realized mainly by a computer program.
The computer readable storage medium may be an internal storage unit of the client described in the foregoing embodiment, such as a hard disk or a memory of the system. The computer readable storage medium may also be an external storage device of the system, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) Card, Flash memory Card (Flash Card), etc. provided on the system. Further, the computer readable storage medium may also include both an internal storage unit and an external storage device of the system. The computer-readable storage medium is used for storing the computer program and other programs and data required by the system. The computer readable storage medium may also be used to temporarily store data that has been output or is to be output.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed units and methods may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may also be an electric, mechanical or other form of connection.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1.一种基于企业征信黑名单的预警提醒方法，其特征在于，包括：1. a kind of early warning reminder method based on enterprise credit blacklist, is characterized in that, comprises: 从多个相关部门提取企业信用数据，并通过网络抓取企业舆情数据；Extract corporate credit data from multiple relevant departments, and capture corporate public opinion data through the Internet; 对所述企业信用数据和企业舆情数据进行清洗及格式化分类处理，得到待处理数据；Cleaning, formatting and classifying the enterprise credit data and enterprise public opinion data to obtain data to be processed; 将所述待处理数据输入已训练的预警分析模型进行预警分析，得到分析结果，并根据所述分析结果进行预警提示。The data to be processed is input into the trained early warning analysis model for early warning analysis, an analysis result is obtained, and an early warning prompt is performed according to the analysis result. 2.如权利要求1所述的基于企业征信黑名单的预警提醒方法，其特征在于，从多个相关部门提取企业信用数据为：2. the early warning reminder method based on enterprise credit blacklist as claimed in claim 1 is characterized in that, extracting enterprise credit data from a plurality of relevant departments is: 从授信平台、工商、法律或税务部门提取企业信用数据。Extract corporate credit data from credit platforms, industry and commerce, legal or taxation departments. 3.如权利要求1所述的基于企业征信黑名单的预警提醒方法，其特征在于，所述企业舆情数据包括舆论信息和公众对有关企业的任何话题的讨论、报道和反映。3 . The method for early warning and reminding based on a blacklist of corporate credit reports according to claim 1 , wherein the corporate public opinion data includes public opinion information and public discussions, reports and reflections on any topic related to the enterprise. 4 . 4.如权利要求1所述的基于企业征信黑名单的预警提醒方法，其特征在于，所述待处理数据包括企业经营数据、企业法律数据、失信数据、财务数据、税务数据和企业高管个人信息；所述预警提醒方法包括：4. The early warning reminder method based on the enterprise credit blacklist as claimed in claim 1, wherein the data to be processed comprises enterprise operation data, enterprise legal data, untrustworthy data, financial data, tax data and enterprise executives Personal information; the early warning and reminder methods include: 将所述企业经营数据、企业法律数据和失信数据输入所述预警分析模型，对企业进行综合评定，判断该企业是否存在欺诈风险，若存在且风险等级较高，则将该企业列入黑名单，并进行风险提示；Input the enterprise operation data, enterprise legal data and untrustworthiness data into the early warning analysis model, conduct a comprehensive assessment of the enterprise, determine whether the enterprise has fraud risks, and if so and the risk level is high, put the enterprise on the blacklist , and make risk warnings; 将所述高管个人信息数据输入所述预警分析模型，判断高管个人是否存在法律风险，若存在法律风险且该法律风险对企业造成影响，则进行风险提示；Input the personal information data of the executive into the early warning analysis model to determine whether the executive has a legal risk, and if there is a legal risk and the legal risk has an impact on the enterprise, a risk warning is issued; 将所述财务数据和税务数据输入所述预警分析模型，判断该企业是否存在财务风险和/或税务风险，若存在且风险值高于预设阈值，则进行风险提示。Input the financial data and tax data into the early warning analysis model to determine whether there is financial risk and/or tax risk in the enterprise, and if there is and the risk value is higher than a preset threshold, a risk alert is performed. 5.如权利要求1-4任一项所述的基于企业征信黑名单的预警提醒方法，其特征在于，所述方法还包括训练预警分析模型，具体为：5. the early warning reminder method based on the enterprise credit blacklist as described in any one of claim 1-4, is characterized in that, described method also comprises training early warning analysis model, is specifically: 基于人工智能技术，构建预警分析模型；Build an early warning analysis model based on artificial intelligence technology; 从相关政府部门、银行机构及授信平台获取样本数据，所述样本数据包括企业信用数据和行业数据；Obtain sample data from relevant government departments, banking institutions and credit platforms, including enterprise credit data and industry data; 采用所述样本数据对所述预警分析模型进行训练。The early warning analysis model is trained by using the sample data. 6.一种基于企业征信黑名单的预警提醒装置，其特征在于，包括：6. An early warning reminder device based on an enterprise credit blacklist, characterized in that, comprising: 数据获取单元，用于从多个相关部门提取企业信用数据，并通过网络抓取企业舆情数据；The data acquisition unit is used to extract corporate credit data from multiple relevant departments, and capture corporate public opinion data through the network; 处理单元，用于对所述企业信用数据和企业舆情数据进行清洗及格式化分类处理，得到待处理数据；a processing unit, used for cleaning, formatting and classifying the enterprise credit data and enterprise public opinion data to obtain data to be processed; 预警分析单元，用于将所述待处理数据输入已训练的预警分析模型进行预警分析，得到分析结果，并根据所述分析结果进行预警提示。An early warning analysis unit, configured to input the data to be processed into a trained early warning analysis model for early warning analysis, obtain an analysis result, and give an early warning prompt according to the analysis result. 7.如权利要求6所述的基于企业征信名单的预警提醒装置，其特征在于，所述待处理数据包括企业经营数据、企业法律数据、失信数据、财务数据、税务数据和企业高管个人信息；所述预警分析单元具体用于：7. The early-warning reminder device based on the enterprise credit information list as claimed in claim 6, wherein the data to be processed includes enterprise operation data, enterprise legal data, untrustworthy data, financial data, tax data and individual enterprise executives information; the early warning analysis unit is specifically used for: 将所述企业经营数据、企业法律数据和失信数据输入所述预警分析模型，对企业进行综合评定，判断该企业是否存在欺诈风险，若存在且风险等级较高，则将该企业列入黑名单，并进行风险提示；Input the enterprise operation data, enterprise legal data and untrustworthiness data into the early warning analysis model, conduct a comprehensive assessment of the enterprise, determine whether the enterprise has fraud risks, and if so and the risk level is high, put the enterprise on the blacklist , and make risk warnings; 将所述高管个人信息数据输入所述预警分析模型，判断高管个人是否存在法律风险，若存在法律风险且该法律风险对企业造成影响，则进行风险提示；Input the personal information data of the executive into the early warning analysis model to determine whether the executive has a legal risk, and if there is a legal risk and the legal risk has an impact on the enterprise, a risk warning is issued; 将所述财务数据和税务数据输入所述预警分析模型，判断该企业是否存在财务风险和/或税务风险，若存在且风险值高于预设阈值，则进行风险提示。Input the financial data and tax data into the early warning analysis model to determine whether there is financial risk and/or tax risk in the enterprise, and if there is and the risk value is higher than a preset threshold, a risk alert is performed. 8.如权利要求6或7所述的基于企业征信黑名单的预警提醒装置，其特征在于，所述装置还包括训练单元，用于训练预警分析模型，具体为：8. The early warning reminder device based on the enterprise credit blacklist as claimed in claim 6 or 7, wherein the device further comprises a training unit for training an early warning analysis model, specifically: 基于人工智能技术，构建预警分析模型；Build an early warning analysis model based on artificial intelligence technology; 从相关政府部门、银行机构及授信平台获取样本数据，所述样本数据包括企业信用数据和行业数据；Obtain sample data from relevant government departments, banking institutions and credit platforms, including enterprise credit data and industry data; 采用所述样本数据对所述预警分析模型进行训练。The early warning analysis model is trained by using the sample data. 9.一种电子设备，包括处理器、输入设备、输出设备和存储器，所述处理器、输入设备、输出设备和存储器通过总线相互连接，所述存储器用于存储计算机程序，所述计算机程序包括程序指令，其特征在于，所述处理器被配置于调用程序指令执行如权利要求5所述的方法。9. An electronic device comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being connected to each other through a bus, the memory being used to store a computer program, the computer program comprising Program instructions, wherein the processor is configured to invoke program instructions to perform the method of claim 5. 10.一种计算机可读存储介质，其上存储有计算机程序/指令，其特征在于，该计算机程序/指令被处理器执行时实现权利要求5所述方法的步骤。10. A computer-readable storage medium on which computer programs/instructions are stored, characterized in that, when the computer programs/instructions are executed by a processor, the steps of the method of claim 5 are implemented.
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